Mobile-based artificial intelligence chatbot for self-regulated learning in a hybrid flipped classroom
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing research on the use of GenAI tools in education, particularly in K-12 contexts, often suffers from methodological limitations, theoretical inconsistencies, and a lack of experimental designs.
“existing research on the use of GenAI tools in education-particularly in K-12 contexts-often suffers from methodological limitations, theoretical inconsistencies, and a lack of experimental designs (X. Han et al. 2025a, b).”
From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as X. Han et al. 2025a, b
Han et al. (2025a, b) showed that AI-supported chatbots effectively enhanced goal setting, monitoring, and reflection in students with initially low SRL skills, but students with high initial SRL levels demonstrated a decrease in strategy use over time.
“On the other hand, Han et al. (2025a, b ) showed that AI-supported chatbots effec -tively enhanced goal setting, monitoring, and reflection in students with initially low SRL skills, but students with high initial SRL levels demonstrated a decrease in strategy use over time, possibly due to overscaffolding.”
From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as Han et al., 2025a, b
AI-supported chatbots effectively enhanced goal setting, monitoring, and reflection in students with initially low SRL skills, whereas students with high initial SRL levels exhibited a decrease in strategy use over time, possibly due to overscaffolding.
“Han et al. ( 2025a, b) found that AI-supported chatbots effectively enhanced goal setting, monitoring, and reflection in students with initially low SRL skills. However, students with high initial SRL levels exhibited a decrease in strategy use over time, possibly due to overscaffolding.”
From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as Han et al. (2025a, b)
The effectiveness of digital learning companions is not universally positive but highly dependent on students' individual learning prerequisites.
“These findings align with recent research showing that the effectiveness of digital learning companions is not universally positive but highly dependent on students' individual learning prerequisites (Han et al. 2025a, b).”
From Enhancing School Students' Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial (2026) · cites this paper as Han et al. 2025a, b
Cites
The Difference between Emergency Remote Teaching and Online Learning
Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2024) · Handbook of Research in Online Learning · doi:10.1163/9789004702813_021 · Cited by 6,253 · see citing works
Do <scp>AI</scp> chatbots improve students learning outcomes? Evidence from a meta‐analysis
Wu, R., & Yu, Z. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13334 · Cited by 576 · cited 1× · see citing works
“Many studies have explored the use of AI chatbots in education and identified a positive impact that is attributed to their adaptability, flexibility, and natural interactions (Essel et al., 2022; Wu & Yu, 2023).”
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00362-6 · Cited by 428 · see citing works
Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2015) · Educational Technology Research and Development · doi:10.1007/s11423-014-9358-1 · Cited by 373 · see citing works
Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education
Lameras, P., & Arnab, S. (2021) · Information · doi:10.3390/info13010014 · Cited by 291 · see citing works
Wang, F. H. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.06.012 · Cited by 252 · see citing works
Hew, K. F., Huang, W., Du, J., & Jia, C. (2023) · Journal of Computing in Higher Education · doi:10.1007/s12528-022-09338-x · Cited by 245 · see citing works
Toward the development of a metacognition construct for communities of inquiry
Garrison, D. R., & Akyol, Z. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2014.10.001 · Cited by 196 · see citing works
Effects of self-regulation scaffolding on online participation and learning outcomes
Song, D., & Kim, D. (2021) · Journal of Research on Technology in Education · doi:10.1080/15391523.2020.1767525 · Cited by 95 · see citing works
Examining the use of prompts to facilitate self-regulated learning in Massive Open Online Courses
Wong, J., Baars, M., de Koning, B. B., & Paas, F. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106596 · Cited by 88 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Fütterer, T., Bardach, L., Kuhn, J., Keller, S. D., & Gerjets, P. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10133-8 · Cited by 17 · see citing works
- Authors
- Han, I., Ji, H., Jin, S., & Choi, K.
- Affiliations
- Korea University (KR)
- Temple University (US)
- Hanyang Cyber University (KR)
- Corresponding author
- Choi, Koun
- Venue
- Journal of Computing in Higher Education
- Published
- 2025-02-20
- Volume
- 38
- Issue
- 1
- Pages
- 285-309
- ISSN
- 1042-1726 (ISSN-L)
- 1867-1233
- OpenAlex
- W4407786377
- Cited by
- 24
- References
- 10
- Citations by year
- 2026: 13 · 2025: 11
- FWCI
- 47.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Computing in Higher Education (publishedVersion)
- Funders
- Ministry of Education (NRF-2023S1A5C2A02095547)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- student self-regulation, AI chatbots, mobile learning, flipped classroom, hybrid learning, higher education, business education, adaptive interventions, personalized learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
